Cookieless Attribution for Mobile Gaming: Measuring Installs and In-App Revenue
Mobile gaming attribution is broken. Cookies are dead, SKAdNetwork is a blunt instrument.
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By Joris van Huët, Founder & CEOUpdated 5 min read
Cookieless Attribution for Mobile Gaming: Measuring Installs and In-App Revenue
Mobile gaming attribution is broken. Apple killed IDFA. Google is phasing out GAID. SKAdNetwork exists but it’s a blunt instrument that reports installs in 24-hour windows, strips creative data, and offers zero visibility into post-install behavior. You’re left guessing which ads drive real revenue, not just installs. The industry’s response? More guesswork dressed up as "probabilistic modeling."
Causal inference fixes this. It doesn’t rely on cookies, device IDs, or last-touch heuristics. It measures the actual lift in installs and in-app revenue caused by your ads. Here’s how.
Why Mobile Gaming Attribution is a Dumpster Fire Right Now
Let’s start with the numbers. Why?
- Lie #1: Last-touch attribution works. In reality, last-touch overcredits retargeting and undercounts prospecting.
- Lie #2: SKAdNetwork is enough. SKAdNetwork’s 6-bit conversion value can’t capture that.
- Lie #3: Probabilistic modeling is accurate. It’s not.
The result? Part of your ad budget may be going to channels that don’t drive incremental revenue.
How Causal Inference Solves Cookieless Attribution for Mobile Gaming
Causal inference doesn’t care about cookies or device IDs. It works by comparing two groups:
- Test group: Users exposed to your ads.
- Control group: Users who would have seen your ads but didn’t (due to holdout experiments or geo-based randomization).
The difference in installs and in-app revenue between these groups is your incremental lift. No last-touch. No SKAdNetwork limitations. No guesswork.
Here’s how it applies to mobile gaming:
1. Measuring True Install Lift
SKAdNetwork tells you how many installs happened after an ad. It doesn’t tell you how many of those installs wouldn’t have happened without the ad. That’s the difference between attributed installs and incremental installs.
2. Capturing Post-Install Revenue
SKAdNetwork’s 6-bit conversion value can’t track IAPs, subscriptions, or ad revenue. It’s like trying to measure a marathon with a ruler.
- Ad exposure → Install → First purchase → Repeat purchases → Subscription upgrades → Ad revenue.
Why?
3. Creative-Level Insights Without Device IDs
SKAdNetwork aggregates creative data at the campaign level. You can’t see which ad variations drive real revenue. Causal inference solves this with geo-based holdouts.
For example, a puzzle game tested two creatives:
- Creative A: "Solve 100 levels in 60 seconds!"
- Creative B: "Unlock a secret level if you beat the clock!"
SKAdNetwork reported similar install rates.
SKAdNetwork is a compliance tool, not a measurement tool. It was designed to protect user privacy, not to help you refine ad spend.
| Metric | SKAdNetwork | Causal Inference |
|---|---|---|
| Revenue measurement | 6-bit conversion value (limited) | Full IAP, subscription, and ad revenue |
| Creative insights | Campaign-level only | Creative-level, ad set-level |
| Incrementality | No | Yes |
How to Implement Cookieless Attribution for Mobile Gaming
Here’s a step-by-step guide to ditching SKAdNetwork’s limitations:
Step 1: Define Your Incrementality Goals
What do you want to measure?
- Incremental IAP revenue
- Incremental ad revenue
- Incremental subscriptions
Step 2: Set Up Holdout Experiments
Use geo-based holdouts or platform-level holdouts (e.g., Meta’s holdout feature). For example:
- Test group: Users in California, Texas, Florida.
- Control group: Users in New York, Illinois, Pennsylvania.
Ensure the groups are statistically similar.
Don’t stop at installs. Track:
- Ad exposure (impressions, clicks)
- Install
- First purchase
- Subscription upgrades
- Ad revenue (for ad-supported games)
Step 4: Measure Incremental Lift
Compare the test group to the control group. The difference is your incremental lift.
Step 5: Refine Based on Incrementality
Allocate budget to the channels, creatives, and audiences driving the highest incremental lift.
FAQs About Cookieless Attribution for Mobile Gaming
What’s the difference between attributed installs and incremental installs?
Attributed installs are installs that happened after an ad was shown. Incremental installs are installs that wouldn’t have happened without the ad. SKAdNetwork measures the former. Causal inference measures the latter.
Can causal inference work without device IDs or cookies?
Yes. Causal inference uses holdout experiments and geo-matching to measure lift without relying on user-level tracking. It’s privacy-safe and works in a cookieless world.
How does causal inference track post-install revenue?
By comparing in-app revenue between test and control groups. For example, if the test group spends $50,000 and the control group spends $20,000, the incremental revenue is $30,000. No device IDs required.
Is causal inference compatible with SKAdNetwork?
Yes. You can use SKAdNetwork for compliance and causal inference for measurement. Think of SKAdNetwork as the speedometer and causal inference as the GPS.
How long does it take to see results with causal inference?
You’ll see initial results within 7-14 days.
Stop Guessing. Start Measuring.
Mobile gaming attribution is broken. Cookies are dead. SKAdNetwork is a blunt instrument. Probabilistic modeling is a joke.
Sources and Further Reading
Related Articles
Frequently asked questions
Does causal inference work for ad-supported games?
Yes. Causal inference measures incremental ad revenue by comparing ad impressions between test and control groups. It works for IAPs, subscriptions, and ad-supported models.Can I use causal inference with MMPs like AppsFlyer or Adjust?
Yes. Causal inference complements MMPs. Use MMPs for compliance and causal inference for incrementality.How does causal inference handle fraud in mobile gaming?
By measuring incremental lift, not attributed installs. Fraudulent installs won’t show up in the control group, so they’re automatically filtered out.
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- Control GroupA control group is the part of an experiment that does not get the treatment being tested, so its outcomes stand in for what the treated group would have had without it. In marketing it is the audience, or the set of regions, held back from a campaign.
- ExperimentsExperiments are scientific procedures that test hypotheses or demonstrate facts. In marketing, experiments like A/B tests determine the causal effect of campaign changes, enabling data-driven decisions.
- IncrementalityIncrementality is the part of an outcome, such as orders or revenue, that a marketing activity caused: the extra results compared with what would have happened without it. It is a causal effect, not the number of conversions credited to a channel.
- Last-Touch AttributionLast-Touch Attribution: A single-touch attribution model that gives 100% of the credit for a conversion to the last marketing touchpoint a customer interacted with.
- RandomizationRandomization: The process of assigning subjects to treatment and control groups by chance. This minimizes confounding and selection bias, allowing for unbiased estimation of treatment effects.
- RetargetingRetargeting is online advertising that targets users who have previously interacted with your website or content. Attribution analysis shows the causal role of retargeting in driving conversions and improving ad spend.